Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection.

Prior studies have shown that sleep duration peri-vaccination influences an individual's antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

Improving participant representation in the era of digital clinical studies.

Traditional clinical research relies on conventional strategies to invite and enroll research participants. However, these strategies often fail to reach potential participants from marginalized communities or that reflect the diversity of the nation, such as race, ethnicity, or geography. As we discuss here, the digital clinical study model sets the stage for improved and equitable participation in biomedical research.

The Power of Patient Engagement With Electronic Health Records as Research Participants.

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands.

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data.

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms.

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

COVID-19 among African Americans and Hispanics: Does gastrointestinal symptoms impact the outcome?

BACKGROUND: The coronavirus disease 2019 (COVID-19) disproportionately affected African Americans (AA) and Hispanics (HSP). AIM: To analyze the significant effectors of outcome in African American patient population and make special emphasis on gastrointestinal (GI) symptoms, laboratory values and comorbidities. METHODS: We retrospectively evaluated the medical records of 386 COVID-19 positive patients admitted at Howard University Hospital between March and May 2020. We assessed the symptoms, including the GI manifestations, comorbidities, and mortality, using logistic regression analysis. RESULTS: Of these 386 COVID-19 positive patients, 257 (63.7%) were AAs, 102 (25.3%) HSP, and 26 (6.45%) Whites. There were 257 (63.7%) AA, 102 (25.3%) HSP, 26 (6.45%) Whites. The mean age was 55.6 years (SD = 18.5). However, the mean age of HSP was the lowest (43.7 years vs 61.2 for Whites vs 60 for AAs). The mortality rate was highest among the AAs (20.6%) and lowest among HSP (6.9%). Patients with shortness of breath (SOB) (OR2 = 3.64, CI = 1.73-7.65) and elevated AST (OR2 = 8.01, CI = 3.79-16.9) elevated Procalcitonin (OR2 = 8.27, CI = 3.95-17.3), AST (OR2 = 8.01, CI = 3.79-16.9), ferritin (OR2 = 2.69, CI = 1.24-5.82), and Lymphopenia (OR2 = 2.77, CI = 1.41-5.45) had a high mortality rate. Cough and fever were common but unrelated to the outcome. Hypertension and diabetes mellitus were the most common comorbidities. Glucocorticoid treatment was associated with higher mortality (OR2 = 5.40, CI = 2.72-10.7). Diarrhea was prevalent (18.8%), and GI symptoms did not affect the outcome. CONCLUSION: African Americans in our study had the highest mortality as they consisted of an older population and comorbidities. Age is the most important factor along with SOB in determining the mortality rate. Overall, elevated liver enzymes, ferritin, procalcitonin and C-reactive protein were associated with poor prognosis. GI symptoms did not affect the outcome. Glucocorticoids should be used judiciously, considering the poor outcomes associated with it. Attention should also be paid to monitor liver function during COVID-19, especially in AA and HSP patients with higher disease severity.

The Physiologic Response to COVID-19 Vaccination.

Two mRNA vaccines and one adenovirus-based vaccine against SARS CoV-2 are currently being distributed at scale in the United States. Objective evidence of a specific individual's physiologic response to that vaccine are not routinely tracked but may offer insights into the acute immune response and personal and/or vaccine characteristics associated with that. We explored this possibility using a smartphone app-based research platform developed early in the pandemic that enabled volunteers (38,911 individuals between 25 March 2020 and 4 April 2021) to share their smartwatch and activity tracker data, as well as self-report, when appropriate, any symptoms, COVID-19 test results and vaccination dates and type. Of 4,110 individuals who reported at least one mRNA vaccination dose, 3,312 provided adequate resting heart rate data from the peri-vaccine period for analysis. We found changes in resting heart rate with respect to an individual baseline increased the days after vaccination, peaked on day 2, and returned to normal on day 6, with a much stronger effect after second dose with respect to first dose (average changes 1.6 versus 0.5 beats per minute). The changes were more pronounced for individuals who received the Moderna vaccine (on both doses), those who previously tested positive to COVID-19 (on dose 1), and for individuals aged <40 years, after adjusting for possible confounding factors. Taking advantage of continuous passive data from personal sensors could potentially enable the identification of a digital fingerprint of inflammation, which might prove useful as a surrogate for vaccine-induced immune response.

Wearable sensor data and self-reported symptoms for COVID-19 detection.

Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.

Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP).

Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.

The effect of variant interference on de novo assembly for viral deep sequencing.

BACKGROUND: Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored. RESULTS: Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This "variant interference" (VI) is highly consistent and reproducible by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the "rescue" of full viral genomes from fragmented contigs. CONCLUSIONS: These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

Genomic characterization of Haemophilus influenzae: a focus on the capsule locus.

BACKGROUND: Haemophilus influenzae (Hi) can cause invasive diseases such as meningitis, pneumonia, or sepsis. Typeable Hi includes six serotypes (a through f), each expressing a unique capsular polysaccharide. The capsule, encoded by the genes within the capsule locus, is a major virulence factor of typeable Hi. Non-typeable (NTHi) does not express capsule and is associated with invasive and non-invasive diseases. METHODS: A total of 395 typeable and 293 NTHi isolates were characterized by whole genome sequencing (WGS). Phylogenetic analysis and multilocus sequence typing were used to characterize the overall genetic diversity. Pair-wise comparisons were used to evaluate the capsule loci. A WGS serotyping method was developed to predict the Hi serotype. WGS serotyping results were compared to slide agglutination (SAST) or real-time PCR (rt-PCR) serotyping. RESULTS: Isolates of each Hi serotype clustered into one or two subclades, with each subclade being associated with a distinct sequence type (ST). NTHi isolates were genetically diverse, with seven subclades and 125 STs being detected. Regions I and III of the capsule locus were conserved among the six serotypes (≥82% nucleotide identity). In contrast, genes in Region II were less conserved, with only six gene pairs from all serotypes showing ≥56% nucleotide identity. The WGS serotyping method was 99.9% concordant with SAST and 100% concordant with rt-PCR in determining the Hi serotype. CONCLUSIONS: Genomic analysis revealed a higher degree of genetic diversity among NTHi compared to typeable Hi. The WGS serotyping method accurately predicted the Hi capsule type and can serve as an alternative method for Hi serotyping.

Whole-Genome Sequencing for Characterization of Capsule Locus and Prediction of Serogroup of Invasive Meningococcal Isolates.

Invasive meningococcal disease is mainly caused by Neisseria meningitidis serogroups A, B, C, X, W, and Y. The serogroup is typically determined by slide agglutination serogrouping (SASG) and real-time PCR (RT-PCR). We describe a whole-genome sequencing (WGS)-based method to characterize the capsule polysaccharide synthesis (cps) locus, classify N. meningitidis serogroups, and identify mechanisms for nongroupability using 453 isolates from a global strain collection. We identified novel genomic organizations within functional cps loci, consisting of insertion sequence (IS) elements in unique positions that did not disrupt the coding sequence. Genetic mutations (partial gene deletion, missing genes, IS insertion, internal stop, and phase-variable off) that led to nongroupability were identified. The results of WGS and SASG were in 91% to 100% agreement for all serogroups, while the results of WGS and RT-PCR showed 99% to 100% agreement. Among isolates determined to be nongroupable by WGS (31 of 453), the results of all three methods agreed 100% for those without a capsule polymerase gene. However, 61% (WGS versus SASG) and 36% (WGS versus RT-PCR) agreements were observed for the isolates, particularly those with phase variations or internal stops in cps loci, which warrant further characterization by additional tests. Our WGS-based serogrouping method provides comprehensive characterization of the N. meningitidis capsule, which is critical for meningococcal surveillance and outbreak investigations.

Analyses of genome wide association data, cytokines, and gene expression in African-Americans with benign ethnic neutropenia.

BACKGROUND: Benign ethnic neutropenia (BEN) is a hematologic condition associated with people of African ancestry and specific Middle Eastern ethnic groups. Prior genetic association studies in large population showed that rs2814778 in Duffy Antigen Receptor for Chemokines (DARC) gene, specifically DARC null red cell phenotype, was associated with BEN. However, the mechanism of this red cell phenotype leading to low white cell count remained elusive. METHODS: We conducted an extreme phenotype design genome-wide association study (GWAS), analyzed ~16 million single nucleotide polymorphisms (SNP) in 1,178 African-Americans individuals from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study and replicated from 819 African-American participants in the Atherosclerosis Risk in Communities (ARIC) study. Conditional analyses on rs2814778 were performed to identify additional association signals on chromosome 1q22. In a separate cohort of healthy individuals with and without BEN, whole genome gene expression from peripheral blood neutrophils were analyzed for DARC. RESULTS: We confirmed that rs2814778 in DARC was associated with BEN (p = 4.09×10-53). Conditioning on rs2814778 abolished other significant chromosome 1 associations. Inflammatory cytokines (IL-2, 6, and 10) in participants in the Howard University Family Study (HUFS) and Multi-Ethnic Study in Atherosclerosis (MESA) showed similar levels in individuals homozygous for the rs2814778 allele compared to others, indicating cytokine sink hypothesis played a minor role in leukocyte homeostasis. Gene expression in neutrophils of individuals with and without BEN was also similar except for low DARC expression in BEN, suggesting normal function. BEN neutrophils had slightly activated profiles in leukocyte migration and hematopoietic stem cell mobilization pathways (expression fold change <2). CONCLUSIONS: These results in humans support the notion of DARC null erythroid progenitors preferentially differentiating to myeloid cells, leading to activated DARC null neutrophils egressing from circulation to the spleen, and causing relative neutropenia. Collectively, these human data sufficiently explained the mechanism DARC null red cell phenotype causing BEN and further provided a biologic basis that BEN is clinically benign.

High-Throughput Next-Generation Sequencing of Polioviruses.

The poliovirus (PV) is currently targeted for worldwide eradication and containment. Sanger-based sequencing of the viral protein 1 (VP1) capsid region is currently the standard method for PV surveillance. However, the whole-genome sequence is sometimes needed for higher resolution global surveillance. In this study, we optimized whole-genome sequencing protocols for poliovirus isolates and FTA cards using next-generation sequencing (NGS), aiming for high sequence coverage, efficiency, and throughput. We found that DNase treatment of poliovirus RNA followed by random reverse transcription (RT), amplification, and the use of the Nextera XT DNA library preparation kit produced significantly better results than other preparations. The average viral reads per total reads, a measurement of efficiency, was as high as 84.2% ± 15.6%. PV genomes covering >99 to 100% of the reference length were obtained and validated with Sanger sequencing. A total of 52 PV genomes were generated, multiplexing as many as 64 samples in a single Illumina MiSeq run. This high-throughput, sequence-independent NGS approach facilitated the detection of a diverse range of PVs, especially for those in vaccine-derived polioviruses (VDPV), circulating VDPV, or immunodeficiency-related VDPV. In contrast to results from previous studies on other viruses, our results showed that filtration and nuclease treatment did not discernibly increase the sequencing efficiency of PV isolates. However, DNase treatment after nucleic acid extraction to remove host DNA significantly improved the sequencing results. This NGS method has been successfully implemented to generate PV genomes for molecular epidemiology of the most recent PV isolates. Additionally, the ability to obtain full PV genomes from FTA cards will aid in facilitating global poliovirus surveillance.

Genomic Test Results and the Courtroom: The Roles of Experts and Expert Testimony.

The rapid advancement from single-gene testing to whole genome sequencing has significantly broadened the type and amount of information available to researchers, physicians, patients, and the public in general. Much debate has ensued about whether genomic test results should be reported to research participants, patients and consumers, and at what stage we can be sure that existing evidence justifies their use in clinical settings. Courts and judges evaluating the utility of these results will not be immune to this uncertainty. As scholars increasingly explore the duty of care standards related to reporting genomic test results, it is timely to provide a framework for understanding how uncertainty about genetic and genomic tests influences evidentiary considerations in the court room. Here, we explore the subtleties and nuances of interpreting genetic data in an environment of substantial discord related to the value that individuals should place on genetic and genomic tests. In conjunction, we discuss the roles courts should play in qualifying experts, expert testimony, and genetic and genomic tests given the intricate and complex nature of genetic and genomic information.

Integrated tRNA, transcript, and protein profiles in response to steroid hormone signaling.

The accurate and efficient transfer of genetic information into amino acid sequences is carried out through codon-anticodon interactions between mRNA and tRNA, respectively. In this way, tRNAs function at the interface between gene expression and protein synthesis. Whether tRNA levels are dynamically regulated and to what degree tRNA abundance influences the cellular proteome remains largely unexplored. Here we profile tRNA, transcript and protein levels in Drosophila Kc167 cells, a plasmatocyte cell line that, upon treatment with 20-hydroxyecdysone, differentiates into macrophages. We find that high abundance tRNAs associate with codons that are overrepresented in the Kc167 cell proteome, whereas tRNAs that are in low supply associate with codons that are underrepresented. Ecdysone-induced differentiation of Kc167 cells leads to changes in mRNA codon usage in a manner consistent with the developmental progression of the cell. At both early and late time points, ecdysone treatment concomitantly increases the abundance of tRNAThr(CGU), which decodes a differentiation-associated codon that becomes enriched in the macrophage proteome. These results together suggest that tRNA levels may provide a meaningful regulatory mechanism for defining the cellular proteomic landscape.

Poly(ADP-ribosyl)ation regulates insulator function and intrachromosomal interactions in Drosophila.

Insulators mediate inter- and intrachromosomal contacts to regulate enhancer-promoter interactions and establish chromosome domains. The mechanisms by which insulator activity can be regulated to orchestrate changes in the function and three-dimensional arrangement of the genome remain elusive. Here, we demonstrate that Drosophila insulator proteins are poly(ADP-ribosyl)ated and that mutation of the poly(ADP-ribose) polymerase (Parp) gene impairs their function. This modification is not essential for DNA occupancy of insulator DNA-binding proteins dCTCF and Su(Hw). However, poly(ADP-ribosyl)ation of K566 in CP190 promotes protein-protein interactions with other insulator proteins, association with the nuclear lamina, and insulator activity in vivo. Consistent with these findings, the nuclear clustering of CP190 complexes is disrupted in Parp mutant cells. Importantly, poly(ADP-ribosyl)ation facilitates intrachromosomal interactions between insulator sites measured by 4C. These data suggest that the role of insulators in organizing the three-dimensional architecture of the genome may be modulated by poly(ADP-ribosyl)ation.